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TiDEC: A Two-Layered Integrated Decision Cycle for Population Evolution

IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 5897-5906页

作者： Ye, Peijun Wang, Xiao Xiong, Gang Chen, Shichao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Agent-based simulation is a useful approach for the analysis of dynamic population evolution. In this field, the existing models mostly treat the migration behavior as a result of utility maximization, which partially ignores the endogenous mechanisms of human decision making. To simulate such a process, this article proposes a new cognitive architecture called the two-layered integrated decision cycle (TiDEC) which characterizes the individual's decision-making process. Different from the previous ones, the new hybrid architecture incorporates deep neural networks for its perception and implicit knowledge learning. The proposed model is applied in China and U.S. population evolution. To the best of our knowledge, this is the first time that the cognitive computation is used in such a field. Computational experiments using the actual census data indicate that the cognitive model, compared with the traditional utility maximization methods, cannot only reconstruct the historical demographic features but also achieve better prediction of future evolutionary dynamics.

关键词： Sociology Statistics Computational modeling Computer architecture Biological system modeling Cognition Decision making Agent-based model (ABM) cognitive architecture population evolution

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Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification 27th

Learning from the Negativity: Deep Negative Correlation Meta...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030678319

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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Cyber-physical -social systems for smart cities: An overview 1

Cyber-physical -social systems for smart cities: An overview

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Anwar, Noreen Xiong, Gang Lu, Wanze Ye, Peijun Zhao, Hongxia Wei, Qinglai Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation The Cloud Computing Center Chinese Academy of Sciences China School of Artificial Intelligence and Automation Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665433372

Cyber-physical social systems (CPSS) include cyberspace, physical space, and social space. Since the introduction of CPSS, human relationships with machinery and the physical world have altered as a new paradigm. Recent estimates indicate that approximately 60 percent of the world's population will live in cities by 2030. Large, dense cities can be highly productive, innovative, and very green per capita, hence ideal for our future. However, the huge influx of new people poses enormous challenges to their administrations. Smart cities are becoming a reality, therefore, to achieve higher efficiency, optimum use of resources, management and to strengthen several features of modern towns, several elements of modern cities are automated and integrated into Information and Communications Technology (ICT). Cities and infrastructure are growing all over the world, in providing end-users with increased performance, comfort, visibility and convenience, the Smart Cities concept is critical. This paper will discuss smart cities and the CPSS concept intensively with some of its key technologies, challenges, and further development by using CPSS. © 2021 IEEE.

关键词： Smart city

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China's 12-Year Quest of Autonomous Vehicular Intelligence: The intelligent Vehicles Future Challenge Program

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IEEE intelligent TRANSPORTATION systems MAGAZINE 2021年第2期13卷 6-19页

作者： Wang, Fei-Yue Zheng, Nan-Ning Li, Li Xin, Jingmin Wang, Xiao Xu, Linhai Tian, Bin Wu, Guozheng Zhang, Zhaotian Wang, Chenghong Chen, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Qingdao Acad Intelligent Ind Beijing 100190 Peoples R China Natl Nat Sci Fdn China Dept Informat Sci Beijing 100085 Peoples R China Chinese Assoc Automat Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

In this article, we introduce the intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests' data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.

关键词： Task analysis Autonomous vehicles Testing Roads Sensors Automation control systems

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialiang Luo Shichao Chen Yisheng Lv Wenqiao Sun Fan Yang Ruijie Xu The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University Jinan China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field.

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Basis Function Matrix-Based Flexible Coefficient Autoregressive Models: A Framework for Time Series and Nonlinear System Modeling

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第2期51卷 614-623页

作者： Chen, Guang-Yong Gan, Min Chen, C. L. Philip Li, Han-Xiong Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350116 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350116 Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework.

关键词： Model building nonlinear least squares (NLLS) parameter estimation system modeling time series

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Improved Vehicle LiDAR Calibration With Trajectory-Based Hand-Eye Method

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第1期23卷 215-224页

作者： Yuwen, Xuan Chen, Long Yan, Fengjun Zhang, Hui Tang, Jianlin Tian, Bin Ai, Yunfeng Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada Jiangsu Xcmg Construct Machinery Res Inst Ltd Inst Intelligent Control Technol Xuzhou 221004 Jiangsu Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Y In unmanned vehicles, LiDARs and GPS/INSs are the most popular sensors to achieve perception and positioning. Precise calibration of the extrinsic parameters between the LiDAR and the GPS/INS is necessary for a successful implementation of sensor fusion. The extrinsic transformation between the LiDAR and GPS/INS is 6D (x, y, z, yaw, pitch, roll), but the motion of a vehicle is mainly 3D (x, y, yaw). The problem is to calculate the 6D extrinsic parameters with the limitation of 3D motion (plane constraint). The solution to this problem has been breaking the plane constraint by designing specific vehicle motions. This paper proposes a new method, a trajectory-based hand-eye calibration method, which makes full use of the large range of unmanned vehicles. The trajectories with large and small ranges are used to solve the rotation and translation, respectively. It is proved that the extrinsic parameters can be solved when the trajectory range of the unmanned vehicle is sufficiently large. The method proposed is tested with simulation, custom and KITTI datasets, and compared with the state-ofthe-art methods. The results demonstrate that the accuracy and efficiency of the method proposed are comparable to the state of the art methods.

关键词： Unmanned vehicles LiDAR calibration trajectory hand-eye model

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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning

Geometry Problem Solving Based on Counter-Factual Evolutiona...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bing Song Gang Xiong Zhen Shen Fenghua Zhu Yisheng Lv Peijun Ye The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.

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Federated Data: Toward New Generation of Credible and Trustable Artificial Intelligence

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第3期8卷 538-545页

作者： Wang, Fei-Yue Zhang, Weishan Tian, Yonglin Qin, Rui Wang, Xiao Hu, Bin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China China Univ Petr Dept Artificial Intelligence Qingdao 266580 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China

Federated ecology can provide an effective solution forthe serious isolated data island issues caused by data privacy protection and information security requirements in the era of artificial intelligence (AI). As the data foundation of federated ecology, federated data include the data of all the nodes in the federation, as wellas their storage, computation, and communication resources. For privacy-preserving, federated data are divided into private data and non-private data, and through the federated control of these data, data federalization can be realized. In data-driven AI technologies, federated data play an important role, and it can help realize effective data retrieval, pre-processing, processing, mining, and visualization for AI-based applications. It can also provide effective solutionsfor the dilemmas faced by AI technologies, such as training AI models without sufficient data, increasing the generality of AI models for different application scenarios and establishing a unified processing workflow for data security and privacy control in AI-based applications.

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Transfer learning for nonlinear batch process operation optimization

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JOURNAL OF PROCESS control 2021年 101卷 11-23页

作者： Chu, Fei Wang, Jiachen Zhao, Xu Zhang, Shuning Chen, Tao Jia, Runda Xiong, Gang China Univ Min & Technol Underground Space Intelligent Control Engn Res Ct Sch Informat & Control Engn Minist Educ Xuzhou 221116 Jiangsu Peoples R China Beijing Gen Res Inst Min & Met State Key Lab Proc Automat Min & Met Beijing Key Lab Proc Automat Min & Met Beijing 100160 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China Univ Surrey Dept Chem & Proc Engn Guildford GU2 7XH Surrey England Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this problem, this paper first adopts the JY-KPLS model to capture the behavior of the nonlinear batch process, and takes full advantage of the information in similar batch processes to assist the modeling and operation optimization of a new process. Then, a data selection based batch-to-batch optimization control strategy is proposed in this paper to reduce the adverse effects of this mismatch on the operation of the new batch process. Finally, the feasibility of the proposed method is demonstrated by simulations. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Transfer learning Nonlinear batch process Insufficient data Operation optimization Data selection

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